
"""simple docstring"""






import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax

					from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel



class 			lowercase					( unittest.TestCase		):






			def __init__(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	List[Any]=13      ,SCREAMING_SNAKE_CASE_  :	int=30      ,SCREAMING_SNAKE_CASE_  :	List[str]=2      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=3      ,SCREAMING_SNAKE_CASE_  :	List[Any]=True      ,SCREAMING_SNAKE_CASE_  :	List[Any]=True      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=32      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=5      ,SCREAMING_SNAKE_CASE_  :	List[str]=4      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=37      ,SCREAMING_SNAKE_CASE_  :	Any="gelu"      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=0.1      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.1      ,SCREAMING_SNAKE_CASE_  :	Any=10      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=0.02      ,)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  parent
				lowerCAmelCase												=  batch_size
				lowerCAmelCase												=  image_size
				lowerCAmelCase												=  patch_size
				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  is_training
				lowerCAmelCase												=  use_labels
				lowerCAmelCase												=  hidden_size
				lowerCAmelCase												=  num_hidden_layers
				lowerCAmelCase												=  num_attention_heads
				lowerCAmelCase												=  intermediate_size
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  type_sequence_label_size
				lowerCAmelCase												=  initializer_range

				# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				lowerCAmelCase												=  (image_size // patch_size) ** 2
				lowerCAmelCase												=  num_patches + 1






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

				lowerCAmelCase												=  ViTConfig(
				    image_size=self.image_size      ,patch_size=self.patch_size      ,num_channels=self.num_channels      ,hidden_size=self.hidden_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,is_decoder=SCREAMING_SNAKE_CASE_      ,initializer_range=self.initializer_range      ,)

				return config, pixel_values






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	Dict			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  FlaxViTModel(config=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_			)
				# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
				lowerCAmelCase												=  (self.image_size, self.image_size)
				lowerCAmelCase												=  (self.patch_size, self.patch_size)
				lowerCAmelCase												=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
				self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, num_patches + 1, self.hidden_size)			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.type_sequence_label_size
				lowerCAmelCase												=  FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_			)
				self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)			)

				# test greyscale images
				lowerCAmelCase												=  1
				lowerCAmelCase												=  FlaxViTForImageClassification(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
				lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	int			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.prepare_config_and_inputs()
				(
				    (
				    lowerCAmelCase
				)							,       (
				    lowerCAmelCase
				)							,       
				)												=  config_and_inputs
				lowerCAmelCase												=  {'''pixel_values''': pixel_values}
				return config, inputs_dict





@require_flax
class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  FlaxViTModelTester(self			)
				lowerCAmelCase												=  ConfigTester(self      ,config_class=SCREAMING_SNAKE_CASE_      ,has_text_modality=SCREAMING_SNAKE_CASE_      ,hidden_size=37			)






			def 					UpperCAmelCase							(self  :	int			)       ->	Optional[int]:





				"""simple docstring"""


				self.config_tester.run_common_tests()






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					lowerCAmelCase												=  model_class(SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  inspect.signature(model.__call__			)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCAmelCase												=  [*signature.parameters.keys()]

					lowerCAmelCase												=  ['''pixel_values''']
					self.assertListEqual(arg_names[:1]      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__			):
						lowerCAmelCase												=  self._prepare_for_class(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
						lowerCAmelCase												=  model_class(SCREAMING_SNAKE_CASE_			)

						@jax.jit
						def model_jitted(SCREAMING_SNAKE_CASE_  :	Optional[int]      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			):
							return model(pixel_values=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

						with self.subTest('''JIT Enabled'''			):
							lowerCAmelCase												=  model_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

						with self.subTest('''JIT Disabled'''			):
							with jax.disable_jit():
								lowerCAmelCase												=  model_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

						self.assertEqual(len(SCREAMING_SNAKE_CASE_			)      ,len(SCREAMING_SNAKE_CASE_			)			)
						for jitted_output, output in zip(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			):
							self.assertEqual(jitted_output.shape      ,output.shape			)






			@slow
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Optional[int]:





				"""simple docstring"""


				for model_class_name in self.all_model_classes:
					lowerCAmelCase												=  model_class_name.from_pretrained('''google/vit-base-patch16-224'''			)
					lowerCAmelCase												=  model(np.ones((1, 3, 224, 224)			)			)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


